Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty qua...Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions.Physiological signals,including Electrocardiogram(ECG),Galvanic Skin Response(GSR),and Electroencephalogram(EEG),were transformed into image representations and analyzed using pretrained deep neu-ral networks.The extracted features were classified through a feedforward neural network,and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout(MCD),model ensembles,and combined approaches.Evaluation metrics included standard measures(sensitivity,specificity,precision,and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision.Across all evaluations,ECG-based models consistently demonstrated strong performance.The findings indicate that combining multimodal physi-ological signals,Transfer Learning(TL),and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems.This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.展开更多
In this paper,we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations(PDEs).The main idea is to use a neural netwo...In this paper,we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations(PDEs).The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion.This map takes input of the element geometry and the PDE’s parameters on that element,and gives output of two operators:(1)the in2out operator for inter-element communication,and(2)the in2sol operator(Green’s function)for element-wise solution recovery.A significant advantage of this approach is that,once trained,this network can be used for the numerical solution of the PDE for any domain geometry and any parameter distribution without retraining.Also,the training is significantly simpler since it is done on the element level instead on the entire domain.We call this approach element learning.This method is closely related to hybridizable discontinuous Galerkin(HDG)methods in the sense that the local solvers of HDG are replaced by machine learning approaches.Numerical tests are presented for an example PDE,the radiative transfer or radiation transport equation,in a variety of scenarios with idealized or realistic cloud fields,with smooth or sharp gradient in the cloud boundary transition.Under a fixed accuracy level of 10^(−3) in the relative L^(2) error,and polynomial degree p=6 in each element,we observe an approximately 5 to 10 times speed-up by element learning compared to a classical finite element-type method.展开更多
Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlo...Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.展开更多
The state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries are crucial for health management and diagnosis.However,most data-driven estimation methods heavily rely on scarce labeled data,while trad...The state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries are crucial for health management and diagnosis.However,most data-driven estimation methods heavily rely on scarce labeled data,while traditional transfer learning faces challenges in handling domain shifts across various battery types.This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries.A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention.Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains,providing a superior starting point for fine-tuning the target domain model.Subsequently,the abundant aging data of the same type as the target battery are labeled through semi-supervised learning,compensating for the source model's limitations in capturing target battery aging characteristics.Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model.In particular,across the experimental groups 13–15 for different types of batteries,the root mean square error of SOH estimation was less than 0.66%,and the mean relative error of RUL estimation was 3.86%.Leveraging extensive unlabeled aging data,the proposed method could achieve accurate estimation of SOH and RUL.展开更多
Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automat...Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.展开更多
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To addres...The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition...Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.展开更多
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work...This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.展开更多
The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model n...The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model needs to be generated which can effectively classify the COVID and non-COVID cases.In this work,our aim is to develop a diagnostic model based on deep features using effectiveness of Chest X-ray(CXR)in distinguishing COVID from non-COVID cases.The proposed diagnostic framework utilizes CXR to diagnose COVID-19 and includes Grad-CAM visualizations for a visual interpretation of predicted images.The model’s performance was evaluated using various metrics,including accuracy,precision,recall,F1-score,and Gmean.Several machine learning models,such as random forest,dense neural network,SVM,twin SVM,extreme learning machine,random vector functional link,and kernel ridge regression,were selected to diagnose COVID-19 cases.Transfer learning was used to extract deep features.For feature extraction many CNN-based models such as Inception V3,MobileNet,ResNet50,VGG16 and Xception models are used.It was evident from the experiments that ResNet50 architecture outperformed all other CNN architectures based on AUC.The TWSVM classifier achieved the highest AUC score of 0.98 based on the ResNet50 feature vector.展开更多
Background:Pneumoconioses,a group of occupational lung diseases caused by inhalation of mineral dust,pose significant health risks to affected individuals.Accurate assessment of profusion(extent of lung involvement)in...Background:Pneumoconioses,a group of occupational lung diseases caused by inhalation of mineral dust,pose significant health risks to affected individuals.Accurate assessment of profusion(extent of lung involvement)in chest radiographs is essential for screening,diagnosis and monitoring of the diseases along with epidemiological classification.This study explores an automated classification system combining U-Net-based segmentation for lung field delineation and DenseNet121 with ImageNet-based transfer learning for profusion classification.Methods:Lung field segmentation using U-Net achieved precise delineation,ensuring accurate region-of-interest definition.Transfer learning with DenseNet121 leveraged pre-trained knowledge from ImageNet,minimizing the need for extensive training.The model was fine-tuned with International Labour Organization(ILO)-2022 version standard chest radiographs and evaluated on a diverse dataset of ILO-2000 version standardized radiographs.Results:The U-Net-based segmentation demonstrated robust performance(Accuracy 94%and Dice Coefficient 90%),facilitating subsequent profusion classification.The DenseNet121-based transfer learning model exhibited high accuracy(95%),precision(92%),and recall(94%)for classifying four profusion levels on test ILO 2000/2011D dataset.The final Evaluation on ILO-2000 radiographs highlighted its generalization capability.Conclusion:The proposed system offers clinical promise,aiding radiologists,pulmonologists,general physicians,and occupational health specialists in pneumoconioses screening,diagnosis,monitoring and epidemiological classification.Best of our knowledge,this is the first work in the field of automated Classification of Profusion in Chest Radiographs of Pneumoconioses based on recently published latest ILO-2022 standard.Future research should focus on further refinement and real-world validation.This approach exemplifies the potential of deep learning for enhancing the accuracy and efficiency of pneumoconioses assessment,benefiting industrial workers,patients,and healthcare providers.展开更多
In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging pr...In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration.In this paper,a novel method named augmented deep sparse autoencoder(ADSAE)is proposed.The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data.This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear.The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions.The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy.This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults.The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods.This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.展开更多
Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an imp...Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Many applications,including security systems,medical diagnostics,and human-computer interfaces,depend on eye gaze recognition.However,due to factors including individual variations,occlusions,and shifting illumination...Many applications,including security systems,medical diagnostics,and human-computer interfaces,depend on eye gaze recognition.However,due to factors including individual variations,occlusions,and shifting illumination conditions,real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition.This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency.Transfer learning is the process of fine-tuning pre-trained models on smaller,domain-specific datasets after they have been trained on larger datasets.We study several transfer learning algorithms and evaluate their effectiveness on eye gaze identification,including both Regression and Classification tasks,using a range of deep learning architectures,namely AlexNet,Visual Geometry Group(VGG),InceptionV3,and ResNet.In this study,we evaluate the effectiveness of transfer learning-basedmodels against models that were trained fromscratch using eye-gazing datasets on grounds of various performance and loss metrics such as Precision,Accuracy,and Mean Absolute Error.We investigate the effects of different pre-trainedmodels,dataset sizes,and domain gaps on the transfer learning process,and the findings of our study clarify the efficacy of transfer learning for eye gaze detection and offer suggestions for the most successful transfer learning strategies to apply in real-world situations.展开更多
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金the Australian Research Council Discovery Projects funding scheme(DP190102181,DP210101465).
文摘Accurate detection of driver fatigue is essential for improving road safety.This study investigates the effectiveness of using multimodal physiological signals for fatigue detection while incorporating uncertainty quantification to enhance the reliability of predictions.Physiological signals,including Electrocardiogram(ECG),Galvanic Skin Response(GSR),and Electroencephalogram(EEG),were transformed into image representations and analyzed using pretrained deep neu-ral networks.The extracted features were classified through a feedforward neural network,and prediction reliability was assessed using uncertainty quantification techniques such as Monte Carlo Dropout(MCD),model ensembles,and combined approaches.Evaluation metrics included standard measures(sensitivity,specificity,precision,and accuracy)along with uncertainty-aware metrics such as uncertainty sensitivity and uncertainty precision.Across all evaluations,ECG-based models consistently demonstrated strong performance.The findings indicate that combining multimodal physi-ological signals,Transfer Learning(TL),and uncertainty quantification can significantly improve both the accuracy and trustworthiness of fatigue detection systems.This approach supports the development of more reliable driver assistance technologies aimed at preventing fatigue-related accidents.
基金partially supported by the NSF(Grant No.DMS 2324368)by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison with funding from the Wisconsin Alumni Research Foundation.
文摘In this paper,we propose a systematic approach for accelerating finite element-type methods by machine learning for the numerical solution of partial differential equations(PDEs).The main idea is to use a neural network to learn the solution map of the PDEs and to do so in an element-wise fashion.This map takes input of the element geometry and the PDE’s parameters on that element,and gives output of two operators:(1)the in2out operator for inter-element communication,and(2)the in2sol operator(Green’s function)for element-wise solution recovery.A significant advantage of this approach is that,once trained,this network can be used for the numerical solution of the PDE for any domain geometry and any parameter distribution without retraining.Also,the training is significantly simpler since it is done on the element level instead on the entire domain.We call this approach element learning.This method is closely related to hybridizable discontinuous Galerkin(HDG)methods in the sense that the local solvers of HDG are replaced by machine learning approaches.Numerical tests are presented for an example PDE,the radiative transfer or radiation transport equation,in a variety of scenarios with idealized or realistic cloud fields,with smooth or sharp gradient in the cloud boundary transition.Under a fixed accuracy level of 10^(−3) in the relative L^(2) error,and polynomial degree p=6 in each element,we observe an approximately 5 to 10 times speed-up by element learning compared to a classical finite element-type method.
基金supported by the National Natural Science Foundation of China(No.52207229)the Key Research and Development Program of Ningxia Hui Autonomous Region of China(No.2024BEE02003)+1 种基金the financial support from the AEGiS Research Grant 2024,University of Wollongong(No.R6254)the financial support from the China Scholarship Council(No.202207550010).
文摘Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion batteries.Although various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for training.Collection of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for years.Here,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL labels.Our approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL labels.The approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge cycles.Our method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional approach.We also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder heads.The projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled data.Our approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
基金supported by the Science and Technology Major Project of Fujian Province of China(Grant No.2022HZ028018)the National Natural Science Foundation of China(Grant No.51907030).
文摘The state of health(SOH)and remaining useful life(RUL)of lithium-ion batteries are crucial for health management and diagnosis.However,most data-driven estimation methods heavily rely on scarce labeled data,while traditional transfer learning faces challenges in handling domain shifts across various battery types.This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries.A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention.Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains,providing a superior starting point for fine-tuning the target domain model.Subsequently,the abundant aging data of the same type as the target battery are labeled through semi-supervised learning,compensating for the source model's limitations in capturing target battery aging characteristics.Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model.In particular,across the experimental groups 13–15 for different types of batteries,the root mean square error of SOH estimation was less than 0.66%,and the mean relative error of RUL estimation was 3.86%.Leveraging extensive unlabeled aging data,the proposed method could achieve accurate estimation of SOH and RUL.
基金The publication of this article is funded by the Qatar National Library.
文摘Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.42307218 and U2239251).
文摘The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training purposes.To address this issue,this study proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(S2SCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been recorded.In the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping mechanism.To illustrate the proposed methodology,an excavation project in Hangzhou,China is adopted.The proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning model.Using the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent excavations.The results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supported by the Natural Science Foundation of Hunan Province of China(Grant No.2021JJ10045).
文摘Aerodynamic evaluation under multi-condition is indispensable for the design of aircraft,and the requirement for mass data still means a high cost.To address this problem,we propose a novel point-cloud multi-condition aerodynamics transfer learning(PCMCA-TL)framework that enables aerodynamic prediction in data-scarce sce-narios by transferring knowledge from well-learned scenarios.We modified the PointNeXt segmentation archi-tecture to a PointNeXtReg+regression model,including a working condition input module.The model is first pre-trained on a public dataset with 2000 shapes but only one working condition and then fine-tuned on a multi-condition small-scale spaceplane dataset.The effectiveness of the PCMCA-TL framework is verified by comparing the pressure coefficients predicted by direct training,pre-training,and TL models.Furthermore,by comparing the aerodynamic force coefficients calculated by predicted pressure coefficients in seconds with the correspond-ing CFD results obtained in hours,the accuracy highlights the development potential of deep transfer learning in aerodynamic evaluation.
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
基金funded by Hanshan Normal University School-Level Research Initiation Program(grant numbers QD202244QD2024207)+3 种基金the Guangdong Higher Education Society’s“Fourteenth Five-Year”Plan 2024 Higher Education Research(grant number 24GYB43)the 2024 Guangdong Provincial Undergraduate Teaching Quality and Teaching Reform Engineering Project:Excellence Program for Cultivating Publicly-Funded Pre-service Teachers for Primary Education in the Context of Rural Revitalizationthe Hanshan Normal University Guangdong East Regional Education Collaborative Innovation Research Centerfunded by these sources.
文摘This study utilized a sequential mediating model to examine the role of motivation to learn and transfer selfefficacy in the relationships between perceived content validity,mentoring function,continuous learning work culture and intention to transfer learning.The sample comprized 429 final-year apprentices in Guangdong province,China(females=69.9%,Engineering&Medicine=69%,mean age=20.99,SD=1.60).The apprentices completed standardized measures of motivation to learn,transfer self-efficacy perceived content validity,mentoring function,and continuous learning work culture.Structural equation modeling was used to analyze the data.Results showed perceived content validity,mentoring function,continuous learning culture to predict intention to transfer learning.Of these factors,perceived content validity was the strongest predictor of intention to transfer learning.Of these factors,perceived content validity was the most influential predictor of intention to transfer learning.The motivation to learn and transfer self-efficacy sequentially mediated the relationship between mentoring function and intention to learning transfer to be stronger than by either alone.Although perceived content validity and continuous learning culture exhibited no significant direct effects on intention to transfer learning,they demonstrated positive indirect associations with intention to transfer via motivation to learn and transfer self-efficacy.These study findings extend the applications of the learning transfer framework to individuals undergoing apprenticeship training which also would apply to other a long-term work-based learning programs.
文摘The COVID-19 pandemic,which was declared by the WHO,had created a global health crisis and disrupted people’s daily lives.A large number of people were affected by the COVID-19 pandemic.Therefore,a diagnostic model needs to be generated which can effectively classify the COVID and non-COVID cases.In this work,our aim is to develop a diagnostic model based on deep features using effectiveness of Chest X-ray(CXR)in distinguishing COVID from non-COVID cases.The proposed diagnostic framework utilizes CXR to diagnose COVID-19 and includes Grad-CAM visualizations for a visual interpretation of predicted images.The model’s performance was evaluated using various metrics,including accuracy,precision,recall,F1-score,and Gmean.Several machine learning models,such as random forest,dense neural network,SVM,twin SVM,extreme learning machine,random vector functional link,and kernel ridge regression,were selected to diagnose COVID-19 cases.Transfer learning was used to extract deep features.For feature extraction many CNN-based models such as Inception V3,MobileNet,ResNet50,VGG16 and Xception models are used.It was evident from the experiments that ResNet50 architecture outperformed all other CNN architectures based on AUC.The TWSVM classifier achieved the highest AUC score of 0.98 based on the ResNet50 feature vector.
文摘Background:Pneumoconioses,a group of occupational lung diseases caused by inhalation of mineral dust,pose significant health risks to affected individuals.Accurate assessment of profusion(extent of lung involvement)in chest radiographs is essential for screening,diagnosis and monitoring of the diseases along with epidemiological classification.This study explores an automated classification system combining U-Net-based segmentation for lung field delineation and DenseNet121 with ImageNet-based transfer learning for profusion classification.Methods:Lung field segmentation using U-Net achieved precise delineation,ensuring accurate region-of-interest definition.Transfer learning with DenseNet121 leveraged pre-trained knowledge from ImageNet,minimizing the need for extensive training.The model was fine-tuned with International Labour Organization(ILO)-2022 version standard chest radiographs and evaluated on a diverse dataset of ILO-2000 version standardized radiographs.Results:The U-Net-based segmentation demonstrated robust performance(Accuracy 94%and Dice Coefficient 90%),facilitating subsequent profusion classification.The DenseNet121-based transfer learning model exhibited high accuracy(95%),precision(92%),and recall(94%)for classifying four profusion levels on test ILO 2000/2011D dataset.The final Evaluation on ILO-2000 radiographs highlighted its generalization capability.Conclusion:The proposed system offers clinical promise,aiding radiologists,pulmonologists,general physicians,and occupational health specialists in pneumoconioses screening,diagnosis,monitoring and epidemiological classification.Best of our knowledge,this is the first work in the field of automated Classification of Profusion in Chest Radiographs of Pneumoconioses based on recently published latest ILO-2022 standard.Future research should focus on further refinement and real-world validation.This approach exemplifies the potential of deep learning for enhancing the accuracy and efficiency of pneumoconioses assessment,benefiting industrial workers,patients,and healthcare providers.
基金supported by the Natural Science Foundation of China(No.51675089).
文摘In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration.In this paper,a novel method named augmented deep sparse autoencoder(ADSAE)is proposed.The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data.This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear.The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions.The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy.This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults.The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods.This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.
基金This work is supported by the National Natural Science Foundation of China(Nos.61771154,61603239,61772454,6171101570).
文摘Deep Learning(DL)is such a powerful tool that we have seen tremendous success in areas such as Computer Vision,Speech Recognition,and Natural Language Processing.Since Automated Modulation Classification(AMC)is an important part in Cognitive Radio Networks,we try to explore its potential in solving signal modulation recognition problem.It cannot be overlooked that DL model is a complex model,thus making them prone to over-fitting.DL model requires many training data to combat with over-fitting,but adding high quality labels to training data manually is not always cheap and accessible,especially in real-time system,which may counter unprecedented data in dataset.Semi-supervised Learning is a way to exploit unlabeled data effectively to reduce over-fitting in DL.In this paper,we extend Generative Adversarial Networks(GANs)to the semi-supervised learning will show it is a method can be used to create a more dataefficient classifier.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
文摘Many applications,including security systems,medical diagnostics,and human-computer interfaces,depend on eye gaze recognition.However,due to factors including individual variations,occlusions,and shifting illumination conditions,real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition.This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency.Transfer learning is the process of fine-tuning pre-trained models on smaller,domain-specific datasets after they have been trained on larger datasets.We study several transfer learning algorithms and evaluate their effectiveness on eye gaze identification,including both Regression and Classification tasks,using a range of deep learning architectures,namely AlexNet,Visual Geometry Group(VGG),InceptionV3,and ResNet.In this study,we evaluate the effectiveness of transfer learning-basedmodels against models that were trained fromscratch using eye-gazing datasets on grounds of various performance and loss metrics such as Precision,Accuracy,and Mean Absolute Error.We investigate the effects of different pre-trainedmodels,dataset sizes,and domain gaps on the transfer learning process,and the findings of our study clarify the efficacy of transfer learning for eye gaze detection and offer suggestions for the most successful transfer learning strategies to apply in real-world situations.
